Distributivity of ordinal sum implications over overlap and grouping functions

summary:In 2015, a new class of fuzzy implications, called ordinal sum implications, was proposed by Su et al. They then discussed the distributivity of such ordinal sum implications with respect to t-norms and t-conorms. In this paper, we continue the study of distributivity of such ordinal sum implications over two newly-born classes of aggregation operators, namely overlap and grouping functions, respectively. The main results of this paper are characterizations of the overlap and/or grouping function solutions to the four usual distributive equations of ordinal sum fuzzy implications. And then sufficient and necessary conditions for ordinal sum implications distributing over overlap and grouping functions are given

Options and Evaluations on Propulsion Systems of LNG Carriers

The LNG carriers are undergoing a period of rapid and profound change, with much larger size ships and novel propulsion systems emerging for fulfilling the market trends of LNG shipping industry. There are various proposed propulsion solutions for LNG carriers, ranging from the conventional steam turbine and dual fuel diesel electric propulsion, until more innovative ideas such as slow speed dual fuel diesel engine, combined gas turbine electric & steam system, and hybrid propulsion based on steam turbine and gas engine. Since propulsion system significantly influenced the ship’s capital, emission regulation compliance and navigation safety, the selection of a proper propulsion option with technical feasibility and economic viability for LNG carriers is currently a major concern from the shipping industry and thus must be comprehensively assessed. In this context, this chapter investigated the main characteristics of these propulsion options in terms of BOG treatment, fuel consumption, emission standards compliance, and plant reliability. Furthermore, comparisons among different propulsion system were also carried out and related evaluation was presented

Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning

Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem by leveraging planning algorithms to plan intermediate subgoals to augment GCRL. Their methods need two crucial requirements: (i) a state representation space to search valid subgoals, and (ii) a distance function to measure the reachability of subgoals. However, they struggle to scale to high-dimensional state space due to their non-compact representations. Moreover, they cannot collect high-quality training data through standard GC policies, which results in an inaccurate distance function. Both affect the efficiency and performance of planning and policy learning. In the paper, we propose a goal-conditioned RL algorithm combined with Disentanglement-based Reachability Planning (REPlan) to solve temporally extended tasks. In REPlan, a Disentangled Representation Module (DRM) is proposed to learn compact representations which disentangle robot poses and object positions from high-dimensional observations in a self-supervised manner. A simple REachability discrimination Module (REM) is also designed to determine the temporal distance of subgoals. Moreover, REM computes intrinsic bonuses to encourage the collection of novel states for training. We evaluate our REPlan in three vision-based simulation tasks and one real-world task. The experiments demonstrate that our REPlan significantly outperforms the prior state-of-the-art methods in solving temporally extended tasks.Comment: Accepted by 2023 RAL with ICR

A novel gas ionization sensor using Pd nanoparticle-capped ZnO

A novel gas ionization sensor using Pd nanoparticle-capped ZnO (Pd/ZnO) nanorods as the anode is proposed. The Pd/ZnO nanorod-based sensors, compared with the bare ZnO nanorod, show lower breakdown voltage for the detected gases with good sensitivity and selectivity. Moreover, the sensors exhibit stable performance after more than 200 tests for both inert and active gases. The simple, low-cost, Pd/ZnO nanorod-based field-ionization gas sensors presented in this study have potential applications in the field of gas sensor devices

Synthesis, Characterization of Metal-Schiff Base Functionalized Mesoporous Silica for Pesticide Adsorption

The mesoporous silica modified by salicylaldimine was prepared through a co-condensation method. Through the bridge effect from metal ion (copper ion, zinc ion, manganese ion), the model drug-avermectin was supported on the metal-Schiff base functionalized mesoporous silica (M-MCM-41) to form a highly efficient adsorbents for pesticidedelivery and removal. The experimental results showed that the sequence in adsorption capacity (AC) for avermectin (AVM) of various mesoporous silica in different adsorption time was Zn-MCM-41 &gt; Cu-MCM-41 &gt; Mn-MCM-41 &gt; MCM-41 &gt; SA-MCM-41. The AC of Zn-MCM-41 was 151 mg/g, while the MCM-41 was 78 mg/g which increased nearly 100 % after modification due to its strongest coordination ability. The FT-IR and XPS results confirmed the existence of coordination bond between SA-MCM-41 and metal ions as well as the coordination bond between M-MCM-41 and avermectin. The kinetic data and the equilibrium isotherms are modeled by three kinetic models, the pseudo-first-order, the pseudo-second-order and intraparticle diffusion, and two isotherm models, Langmuir and Freundlich, respectively. The kinetic mechanism of the adoption changed from physical adsorption to the intraparticle diffusion due to the stronger interaction between avermectin and the mesoporous silica through coordination. Adsorption isotherm of the samples were well-represented by Freundlich model which indicated that the adsorption was reversible. </p

Observation of spin-orbit magnetoresistance in metallic thin films on magnetic insulators

A magnetoresistance effect induced by the Rashba spin-orbit interaction was predicted, but not yet observed, in bilayers consisting of normal metal and ferromagnetic insulator. Here, we present an experimental observation of this new type of spin-orbit magnetoresistance (SOMR) effect in a bilayer structure Cu[Pt]/Y3Fe5O12 (YIG), where the Cu/YIG interface is decorated with nanosize Pt islands. This new MR is apparently not caused by the bulk spin-orbit interaction because of the negligible spin-orbit interaction in Cu and the discontinuity of the Pt islands. This SOMR disappears when the Pt islands are absent or located away from the Cu/YIG interface, therefore we can unambiguously ascribe it to the Rashba spin-orbit interaction at the interface enhanced by the Pt decoration. The numerical Boltzmann simulations are consistent with the experimental SOMR results in the angular dependence of magnetic field and the Cu thickness dependence. Our finding demonstrates the realization of the spin manipulation by interface engineering.Comment: 12 pages, 4 figures, 14 pages in supplementary. To appear on Science Advance

Selection of Reference Genes for RT-qPCR Analysis in \u3cem\u3eCoccinella septempunctata\u3c/em\u3e\u3c to Assess Un-intended Effects of RNAi Transgenic Plants

The development of genetically engineered plants that employ RNA interference (RNAi) to suppress invertebrate pests opens up new avenues for insect control. While this biotechnology shows tremendous promise, the potential for both non-target and off-target impacts, which likely manifest via altered mRNA expression in the exposed organisms, remains a major concern. One powerful tool for the analysis of these un-intended effects is reverse transcriptase-quantitative polymerase chain reaction, a technique for quantifying gene expression using a suite of reference genes for normalization. The seven-spotted ladybeetle Coccinella septempunctata, a commonly used predator in both classical and augmentative biological controls, is a model surrogate species used in the environmental risk assessment (ERA) of plant incorporated protectants (PIPs). Here, we assessed the suitability of eight reference gene candidates for the normalization and analysis of C. septempunctata v-ATPase A gene expression under both biotic and abiotic conditions. Five computational tools with distinct algorisms, geNorm, Normfinder, BestKeeper, the ΔCtmethod, and RefFinder, were used to evaluate the stability of these candidates. As a result, unique sets of reference genes were recommended, respectively, for experiments involving different developmental stages, tissues, and ingested dsRNAs. By providing a foundation for standardized RT-qPCR analysis in C. septempunctata, our work improves the accuracy and replicability of the ERA of PIPs involving RNAi transgenic plants